give me an ai code in c# in csharp

using System;

class NeuralNetwork
{
    static void Main()
    {
        // Input data
        double[][] input = new double[4][];
        input[0] = new double[] { 0, 0 };
        input[1] = new double[] { 0, 1 };
        input[2] = new double[] { 1, 0 };
        input[3] = new double[] { 1, 1 };

        // Output data
        double[][] output = new double[4][];
        output[0] = new double[] { 0 };
        output[1] = new double[] { 1 };
        output[2] = new double[] { 1 };
        output[3] = new double[] { 0 };

        // Create a neural network with 2 input nodes, 1 hidden node, and 1 output node
        NeuralNetwork nn = new NeuralNetwork(2, 1, 1);

        // Train the neural network using backpropagation
        nn.Train(input, output, 10000, 0.1);

        // Make a prediction
        double[] prediction = nn.Predict(new double[] { 0, 1 });
        Console.WriteLine("Prediction: {0}", prediction[0]);
        Console.ReadKey();
    }

    private double[][][] weights; // weights[i][j][k] = weight from neuron j in layer i to neuron k in layer i+1
    private double[][] neurons; // neurons[i][j] = output of neuron j in layer i
    private int[] layers; // layers[i] = number of neurons in layer i

    // Create a neural network with given number of layers and neurons in each layer
    public NeuralNetwork(params int[] layers)
    {
        // Initialize layers and neurons arrays
        this.layers = new int[layers.Length];
        for (int i = 0; i < layers.Length; i++)
        {
            this.layers[i] = layers[i];
        }

        neurons = new double[layers.Length][];
        for (int i = 0; i < layers.Length; i++)
        {
            neurons[i] = new double[layers[i]];
        }

        // Initialize weights array
        weights = new double[layers.Length - 1][][];
        for (int i = 1; i < layers.Length; i++)
        {
            weights[i - 1] = new double[layers[i]][];
            for (int j = 0; j < layers[i]; j++)
            {
                weights[i - 1][j] = new double[layers[i - 1]];
                for (int k = 0; k < layers[i - 1]; k++)
                {
                    weights[i - 1][j][k] = GaussianRandom();
                }
            }
        }
    }

    // Train the neural network using backpropagation
    public void Train(double[][] input, double[][] output, int iterations, double learningRate)
    {
        for (int iter = 0; iter < iterations; iter++)
        {
            for (int i = 0; i < input.Length; i++)
            {
                Predict(input[i]);

                // Backpropagation
                double[][] delta = new double[layers.Length][];
                for (int j = 0; j < layers.Length; j++)
                {
                    delta[j] = new double[layers[j]];
                }

                for (int j = 0; j < neurons[neurons.Length - 1].Length; j++)
                {
                    delta[neurons.Length - 1][j] = neurons[neurons.Length - 1][j] - output[i][j];
                    for (int k = 0; k < neurons[neurons.Length - 2].Length; k++)
                    {
                        weights[weights.Length - 1][j][k] -= learningRate * delta[neurons.Length - 1][j] * neurons[neurons.Length - 2][k];
                    }
                }

                for (int j = neurons.Length - 2; j >= 1; j--)
                {
                    for (int k = 0; k < neurons[j].Length; k++)
                    {
                        double sum = 0.0;
                        for (int l = 0; l < neurons[j + 1].Length; l++)
                        {
                            sum += weights[j][l][k] * delta[j + 1][l];
                        }
                        delta[j][k] = neurons[j][k] * (1.0 - neurons[j][k]) * sum;
                        for (int l = 0; l < neurons[j - 1].Length; l++)
                        {
                            weights[j - 1][k][l] -= learningRate * delta[j][k] * neurons[j - 1][l];
                        }
                    }
                }
            }
        }
    }

    // Make a prediction
    public double[] Predict(double[] input)
    {
        // Set input layer
        for (int i = 0; i < input.Length; i++)
        {
            neurons[0][i] = input[i];
        }

        // Feedforward
        for (int i = 1; i < layers.Length; i++)
        {
            for (int j = 0; j < neurons[i].Length; j++)
            {
                double sum = 0.0;
                for (int k = 0; k < neurons[i - 1].Length; k++)
                {
                    sum += weights[i - 1][j][k] * neurons[i - 1][k];
                }
                neurons[i][j] = Sigmoid(sum);
            }
        }

        // Return output layer
        return neurons[neurons.Length - 1];
    }

    // Sigmoid activation function
    private static double Sigmoid(double x)
    {
        return 1.0 / (1.0 + Math.Exp(-x));
    }

    // Gaussian random number generator
    private static Random random = new Random();
    private static double GaussianRandom()
    {
        double u1 = 1.0 - random.NextDouble();
        double u2 = 1.0 - random.NextDouble();
        double randStdNormal = Math.Sqrt(-2.0 * Math.Log(u1)) * Math.Sin(2.0 * Math.PI * u2);
        return randStdNormal;
    }
}
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